Big Data Considerations in Retail

In today's Digital age, Retailers are choking on data which has grown exponentially over the years. Big Data solutions provide a platform for seamlessly analyzing large volumes of structured and unstructured data at a fraction of cost and time using a very scalable infrastructure and generating meaningful business analytics and is poised to transform the Retail world beyond anyone's imagination. How do you beat this combination - An intelligent Data Warehouse (not following any particular schema!) plus incredibly powerful yet cost effective array of distributed processors with built in DR , generating meaningful analytics? As an example, with Big Data solutions, Retailers can now predict demand by combining purchase history, social media trends, online browsing behavior, market data and also personalize shopping experience for the customers. Another example would be analyzing POS transactions for understanding effectiveness of promotions or predicting network fault, failures proactively. An emerging trend is "Data Sandboxes" where Business users can analyze and mine the data relationship and determine if it yields meaningful insights prior to publishing it to the Data Warehouse. Big Data has emerged from the need to analyze terabytes and petabytes of data across various channels which otherwise would be extremely complex and expensive. Big Data solutions should not be seen as a substitute for the organization "think tank", it is an enabler or an engine which provides data to the business to make right decisions. A good Big data solution should be able to handle high data velocity (high input rate), data variety (structured, unstructured), large data volumes, complexity ( cloud, on premise, hybrid), be cost efficient and scalable. Organizations have to carefully charter their Big Data Strategy which requires involvement of both technical and business teams. The focus should not shift from solving a business problem to technology in isolation.